首页|NSW Health Pathology Reports Findings in Clinical Chemistry and Laboratory Medic ine (Multivariate anomaly detection models enhance identification of errors in r outine clinical chemistry testing)
NSW Health Pathology Reports Findings in Clinical Chemistry and Laboratory Medic ine (Multivariate anomaly detection models enhance identification of errors in r outine clinical chemistry testing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Health and Medicine-Clinical Chemistry and Laboratory Medicine is the subject of a report. According to news reporting from Liverpool, Australia, by NewsRx editors, the research st ated, "Conventional autoverification rules evaluate analytes independently, pote ntially missing unusual patterns of results indicative of errors such as serum c ontamination by collection tube additives. This study assessed whether multivari ate anomaly detection algorithms could enhance the detection of such errors." The news correspondents obtained a quote from the research from NSW Health Patho logy, "Multivariate Gaussian, k-nearest neighbours (KNN) distance, and one-class support vector machine (SVM) anomaly detection models, along with conventional limit checks, were developed using a training dataset of 127,451 electrolyte, ur ea, and creatinine (EUC) results, with a 5 % flagging rate targete d for all approaches. The models were compared with limit checks for their abili ty to detect atypical EUC results from samples spiked with additives from collec tion tubes: EDTA, fluoride, sodium citrate, or acid citrate dextrose (n=200 per contaminant). The study additionally assessed the ability of the models to ident ify 127,449 single-analyte errors, a potential weakness of multivariate models. The KNN distance and SVM models outperformed limit checks for detecting all cont aminants (p-values <0.05). The multivariate Gaussian model did not surpass limit checks for detecting EDTA contamination but was superior f or detecting the other additives. All models surpassed limit checks for identify ing single-analyte errors, with the KNN distance model demonstrating the highest overall sensitivity. Multivariate anomaly detection models, particularly the KN N distance model, were superior to the conventional approach for detecting serum contamination and single-analyte errors."
LiverpoolAustraliaAustralia and New ZealandClinical ChemistryClinical Chemistry and Laboratory MedicineHealth and Medicine
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